Overview

Brought to you by YData

Dataset statistics

Number of variables59
Number of observations14216
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory31.7 MiB
Average record size in memory2.3 KiB

Variable types

Numeric5
Categorical22
Text7
DateTime11
Unsupported13
Boolean1

Alerts

Site Code has constant value "20" Constant
Cancellation Reason on Surgery Day has constant value "" Constant
Readmission has constant value "0" Constant
No Show has constant value "False" Constant
Activity Code is highly overall correlated with Activity Type CodeHigh correlation
Activity Type Code is highly overall correlated with Activity CodeHigh correlation
Actual Operating Room Number is highly overall correlated with Planned Operating Room NumberHigh correlation
Actual Surgery Room Entry Time_Minutes is highly overall correlated with Administrative Admission Time_Minutes and 9 other fieldsHigh correlation
Administrative Admission Time_Minutes is highly overall correlated with Actual Surgery Room Entry Time_Minutes and 6 other fieldsHigh correlation
Anesthesia Code is highly overall correlated with Type of AnesthesiaHigh correlation
Closure Time_Minutes is highly overall correlated with Actual Surgery Room Entry Time_Minutes and 9 other fieldsHigh correlation
End of Surgery Time (Exit from OR)_Minutes is highly overall correlated with Actual Surgery Room Entry Time_Minutes and 9 other fieldsHigh correlation
Incision Time_Minutes is highly overall correlated with Actual Surgery Room Entry Time_Minutes and 9 other fieldsHigh correlation
Planned End Time for Doctor Block_Minutes is highly overall correlated with Actual Surgery Room Entry Time_Minutes and 6 other fieldsHigh correlation
Planned Operating Room Number is highly overall correlated with Actual Operating Room NumberHigh correlation
Planned Start Time for Doctor Block_Minutes is highly overall correlated with Actual Surgery Room Entry Time_Minutes and 6 other fieldsHigh correlation
Planned Surgery Time_Minutes is highly overall correlated with Actual Surgery Room Entry Time_Minutes and 9 other fieldsHigh correlation
Pre-Surgical Admission Time Before Surgery_Minutes is highly overall correlated with Actual Surgery Room Entry Time_Minutes and 9 other fieldsHigh correlation
Recovery Room Entry Time_Minutes is highly overall correlated with Actual Surgery Room Entry Time_Minutes and 9 other fieldsHigh correlation
Recovery Room Exit Time_Minutes is highly overall correlated with Actual Surgery Room Entry Time_Minutes and 6 other fieldsHigh correlation
Type of Anesthesia is highly overall correlated with Anesthesia CodeHigh correlation
Anesthesia Code is highly imbalanced (77.6%) Imbalance
Type of Anesthesia is highly imbalanced (77.6%) Imbalance
Activity Code is highly skewed (γ1 = 119.2237364) Skewed
patient_id has unique values Unique
strict_signature has unique values Unique
Planned Surgery Time is an unsupported type, check if it needs cleaning or further analysis Unsupported
Administrative Admission Time is an unsupported type, check if it needs cleaning or further analysis Unsupported
Pre-Surgery Hospitalization Admission Time is an unsupported type, check if it needs cleaning or further analysis Unsupported
Pre-Surgical Admission Time Before Surgery is an unsupported type, check if it needs cleaning or further analysis Unsupported
Actual Surgery Room Entry Time is an unsupported type, check if it needs cleaning or further analysis Unsupported
Incision Time is an unsupported type, check if it needs cleaning or further analysis Unsupported
Closure Time is an unsupported type, check if it needs cleaning or further analysis Unsupported
End of Surgery Time (Exit from OR) is an unsupported type, check if it needs cleaning or further analysis Unsupported
Recovery Room Entry Time is an unsupported type, check if it needs cleaning or further analysis Unsupported
Recovery Room Exit Time is an unsupported type, check if it needs cleaning or further analysis Unsupported
Post-Surgery Discharge Time is an unsupported type, check if it needs cleaning or further analysis Unsupported
Planned Start Time for Doctor Block is an unsupported type, check if it needs cleaning or further analysis Unsupported
Planned End Time for Doctor Block is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2025-04-20 18:17:33.111626
Analysis finished2025-04-20 18:20:36.513972
Duration3 minutes and 3.4 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

patient_id
Real number (ℝ)

Unique 

Distinct14216
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21800.211
Minimum1
Maximum44045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size222.1 KiB
2025-04-20T18:20:36.590542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2553.5
Q111659.75
median21994.5
Q332703.25
95-th percentile40265.5
Maximum44045
Range44044
Interquartile range (IQR)21043.5

Descriptive statistics

Standard deviation12226.126
Coefficient of variation (CV)0.56082601
Kurtosis-1.2172491
Mean21800.211
Median Absolute Deviation (MAD)10525
Skewness-0.062229152
Sum3.099118 × 108
Variance1.4947815 × 108
MonotonicityStrictly increasing
2025-04-20T18:20:36.691810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44045 1
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
7 1
 
< 0.1%
9 1
 
< 0.1%
26 1
 
< 0.1%
38 1
 
< 0.1%
39 1
 
< 0.1%
43921 1
 
< 0.1%
43912 1
 
< 0.1%
Other values (14206) 14206
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
26 1
< 0.1%
38 1
< 0.1%
39 1
< 0.1%
40 1
< 0.1%
41 1
< 0.1%
42 1
< 0.1%
ValueCountFrequency (%)
44045 1
< 0.1%
44042 1
< 0.1%
44037 1
< 0.1%
44031 1
< 0.1%
44028 1
< 0.1%
44027 1
< 0.1%
44016 1
< 0.1%
44007 1
< 0.1%
44006 1
< 0.1%
44003 1
< 0.1%

Site Code
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size930.1 KiB
20
14216 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters28432
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20
2nd row20
3rd row20
4th row20
5th row20

Common Values

ValueCountFrequency (%)
20 14216
100.0%

Length

2025-04-20T18:20:36.774587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T18:20:36.827102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
20 14216
100.0%

Most occurring characters

ValueCountFrequency (%)
2 14216
50.0%
0 14216
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28432
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 14216
50.0%
0 14216
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28432
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 14216
50.0%
0 14216
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28432
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 14216
50.0%
0 14216
50.0%
Distinct495
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size973.6 KiB
2025-04-20T18:20:37.051816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length5
Mean length5.1310495
Min length4

Characters and Unicode

Total characters72943
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)0.6%

Sample

1st row-999, 30817
2nd row-999, 18731
3rd row-999, 18796
4th row-999, 31448
5th row-999, 23054
ValueCountFrequency (%)
19311 460
 
3.2%
20044 312
 
2.2%
19937 231
 
1.6%
20636 227
 
1.6%
23095 209
 
1.5%
22241 188
 
1.3%
27437 157
 
1.1%
27134 156
 
1.1%
17276 154
 
1.1%
18172 150
 
1.1%
Other values (478) 11980
84.2%
2025-04-20T18:20:37.379787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 12977
17.8%
2 11379
15.6%
3 9241
12.7%
7 6262
8.6%
4 6205
8.5%
9 5917
8.1%
0 5590
7.7%
6 5395
7.4%
5 5153
 
7.1%
8 4803
 
6.6%
Other values (3) 21
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72943
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 12977
17.8%
2 11379
15.6%
3 9241
12.7%
7 6262
8.6%
4 6205
8.5%
9 5917
8.1%
0 5590
7.7%
6 5395
7.4%
5 5153
 
7.1%
8 4803
 
6.6%
Other values (3) 21
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72943
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 12977
17.8%
2 11379
15.6%
3 9241
12.7%
7 6262
8.6%
4 6205
8.5%
9 5917
8.1%
0 5590
7.7%
6 5395
7.4%
5 5153
 
7.1%
8 4803
 
6.6%
Other values (3) 21
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72943
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 12977
17.8%
2 11379
15.6%
3 9241
12.7%
7 6262
8.6%
4 6205
8.5%
9 5917
8.1%
0 5590
7.7%
6 5395
7.4%
5 5153
 
7.1%
8 4803
 
6.6%
Other values (3) 21
 
< 0.1%

Activity Code
Real number (ℝ)

High correlation  Skewed 

Distinct357
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.347863
Minimum1.2
Maximum364308
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size222.1 KiB
2025-04-20T18:20:37.470864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile22.091
Q151.23
median57.321
Q380.25
95-th percentile85.432
Maximum364308
Range364306.8
Interquartile range (IQR)29.02

Descriptive statistics

Standard deviation3055.0439
Coefficient of variation (CV)35.795201
Kurtosis14214.866
Mean85.347863
Median Absolute Deviation (MAD)11.87
Skewness119.22374
Sum1213305.2
Variance9333293.2
MonotonicityNot monotonic
2025-04-20T18:20:37.567521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51.23 789
 
5.6%
81.54 646
 
4.5%
60.2 544
 
3.8%
85.432 530
 
3.7%
53 485
 
3.4%
57 474
 
3.3%
42.3 429
 
3.0%
54.01 428
 
3.0%
81.01 414
 
2.9%
65.21 406
 
2.9%
Other values (347) 9071
63.8%
ValueCountFrequency (%)
1.2 3
 
< 0.1%
2.3 7
< 0.1%
3.4 15
0.1%
4.43 16
0.1%
4.431 2
 
< 0.1%
4.433 3
 
< 0.1%
4.46 4
 
< 0.1%
4.49 1
 
< 0.1%
4.5 17
0.1%
4.84 1
 
< 0.1%
ValueCountFrequency (%)
364308 1
 
< 0.1%
98.55 87
0.6%
98.52 2
 
< 0.1%
98.51 9
 
0.1%
97.71 1
 
< 0.1%
96.23 2
 
< 0.1%
86.833 1
 
< 0.1%
86.83 13
 
0.1%
86.82 1
 
< 0.1%
86.4 20
 
0.1%

Activity Type Code
Categorical

High correlation 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size929.9 KiB
12
5482 
15
2652 
17
2195 
22
1923 
19
1498 
Other values (8)
 
466

Length

Max length3
Median length2
Mean length1.9829769
Min length1

Characters and Unicode

Total characters28190
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row15
2nd row22
3rd row22
4th row22
5th row22

Common Values

ValueCountFrequency (%)
12 5482
38.6%
15 2652
18.7%
17 2195
15.4%
22 1923
 
13.5%
19 1498
 
10.5%
8 246
 
1.7%
51 102
 
0.7%
14 42
 
0.3%
13 37
 
0.3%
20 30
 
0.2%
Other values (3) 9
 
0.1%

Length

2025-04-20T18:20:37.669721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12 5482
38.6%
15 2652
18.7%
17 2195
15.4%
22 1923
 
13.5%
19 1498
 
10.5%
8 246
 
1.7%
51 102
 
0.7%
14 42
 
0.3%
13 37
 
0.3%
20 30
 
0.2%
Other values (3) 9
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 12015
42.6%
2 9358
33.2%
5 2758
 
9.8%
7 2195
 
7.8%
9 1503
 
5.3%
8 246
 
0.9%
3 43
 
0.2%
4 42
 
0.1%
0 30
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28190
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 12015
42.6%
2 9358
33.2%
5 2758
 
9.8%
7 2195
 
7.8%
9 1503
 
5.3%
8 246
 
0.9%
3 43
 
0.2%
4 42
 
0.1%
0 30
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28190
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 12015
42.6%
2 9358
33.2%
5 2758
 
9.8%
7 2195
 
7.8%
9 1503
 
5.3%
8 246
 
0.9%
3 43
 
0.2%
4 42
 
0.1%
0 30
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28190
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 12015
42.6%
2 9358
33.2%
5 2758
 
9.8%
7 2195
 
7.8%
9 1503
 
5.3%
8 246
 
0.9%
3 43
 
0.2%
4 42
 
0.1%
0 30
 
0.1%
Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size956.4 KiB
20.0
9132 
0.0
 
776
18.0
 
455
15.0
 
443
17.0
 
442
Other values (17)
2968 

Length

Max length4
Median length4
Mean length3.8932893
Min length0

Characters and Unicode

Total characters55347
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20.0
2nd row20.0
3rd row0.0
4th row20.0
5th row20.0

Common Values

ValueCountFrequency (%)
20.0 9132
64.2%
0.0 776
 
5.5%
18.0 455
 
3.2%
15.0 443
 
3.1%
17.0 442
 
3.1%
16.0 433
 
3.0%
19.0 416
 
2.9%
14.0 402
 
2.8%
13.0 352
 
2.5%
12.0 314
 
2.2%
Other values (12) 1051
 
7.4%

Length

2025-04-20T18:20:37.755153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20.0 9132
64.5%
0.0 776
 
5.5%
18.0 455
 
3.2%
15.0 443
 
3.1%
17.0 442
 
3.1%
16.0 433
 
3.1%
19.0 416
 
2.9%
14.0 402
 
2.8%
13.0 352
 
2.5%
12.0 314
 
2.2%
Other values (11) 1002
 
7.1%

Most occurring characters

ValueCountFrequency (%)
0 24274
43.9%
. 14167
25.6%
2 9468
 
17.1%
1 3997
 
7.2%
9 564
 
1.0%
8 562
 
1.0%
7 517
 
0.9%
6 504
 
0.9%
5 483
 
0.9%
4 436
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 55347
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 24274
43.9%
. 14167
25.6%
2 9468
 
17.1%
1 3997
 
7.2%
9 564
 
1.0%
8 562
 
1.0%
7 517
 
0.9%
6 504
 
0.9%
5 483
 
0.9%
4 436
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 55347
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 24274
43.9%
. 14167
25.6%
2 9468
 
17.1%
1 3997
 
7.2%
9 564
 
1.0%
8 562
 
1.0%
7 517
 
0.9%
6 504
 
0.9%
5 483
 
0.9%
4 436
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 55347
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 24274
43.9%
. 14167
25.6%
2 9468
 
17.1%
1 3997
 
7.2%
9 564
 
1.0%
8 562
 
1.0%
7 517
 
0.9%
6 504
 
0.9%
5 483
 
0.9%
4 436
 
0.8%
Distinct14205
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23743921
Minimum23306822
Maximum85473977
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size222.1 KiB
2025-04-20T18:20:37.836864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum23306822
5-th percentile23449474
Q123453243
median23458027
Q323462767
95-th percentile23466648
Maximum85473977
Range62167155
Interquartile range (IQR)9524.5

Descriptive statistics

Standard deviation3344346.5
Coefficient of variation (CV)0.14085064
Kurtosis138.2271
Mean23743921
Median Absolute Deviation (MAD)4760.5
Skewness11.741042
Sum3.3754358 × 1011
Variance1.1184653 × 1013
MonotonicityNot monotonic
2025-04-20T18:20:37.935246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23463626 2
 
< 0.1%
23463502 2
 
< 0.1%
23463633 2
 
< 0.1%
23463305 2
 
< 0.1%
23463352 2
 
< 0.1%
23463571 2
 
< 0.1%
23463577 2
 
< 0.1%
23463480 2
 
< 0.1%
23455521 2
 
< 0.1%
23463698 2
 
< 0.1%
Other values (14195) 14196
99.9%
ValueCountFrequency (%)
23306822 1
< 0.1%
23316619 1
< 0.1%
23322684 1
< 0.1%
23323171 1
< 0.1%
23323482 1
< 0.1%
23445489 1
< 0.1%
23446072 1
< 0.1%
23446441 1
< 0.1%
23447029 1
< 0.1%
23447085 1
< 0.1%
ValueCountFrequency (%)
85473977 1
< 0.1%
85471171 1
< 0.1%
69503618 1
< 0.1%
62463788 1
< 0.1%
62463754 1
< 0.1%
62463655 1
< 0.1%
62463590 1
< 0.1%
62463525 1
< 0.1%
62463386 1
< 0.1%
62463339 1
< 0.1%
Distinct282
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size222.1 KiB
Minimum2009-10-29 00:00:00
Maximum2017-12-28 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-20T18:20:38.030201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:20:38.148562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Height
Text

Distinct95
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size955.2 KiB
2025-04-20T18:20:38.300106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.8017727
Min length0

Characters and Unicode

Total characters54046
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)0.2%

Sample

1st row1.57
2nd row1.7
3rd row1.65
4th row1.65
5th row1.65
ValueCountFrequency (%)
1.7 999
 
7.0%
1.6 933
 
6.6%
1.65 802
 
5.7%
1.68 650
 
4.6%
1.62 599
 
4.2%
1.8 554
 
3.9%
1.64 547
 
3.9%
1.75 536
 
3.8%
1.78 529
 
3.7%
1.72 513
 
3.6%
Other values (84) 7532
53.1%
2025-04-20T18:20:38.517482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 14836
27.5%
. 14194
26.3%
6 6517
12.1%
7 5651
 
10.5%
5 4072
 
7.5%
8 3448
 
6.4%
2 1527
 
2.8%
4 1425
 
2.6%
3 1355
 
2.5%
9 1003
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54046
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 14836
27.5%
. 14194
26.3%
6 6517
12.1%
7 5651
 
10.5%
5 4072
 
7.5%
8 3448
 
6.4%
2 1527
 
2.8%
4 1425
 
2.6%
3 1355
 
2.5%
9 1003
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54046
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 14836
27.5%
. 14194
26.3%
6 6517
12.1%
7 5651
 
10.5%
5 4072
 
7.5%
8 3448
 
6.4%
2 1527
 
2.8%
4 1425
 
2.6%
3 1355
 
2.5%
9 1003
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54046
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 14836
27.5%
. 14194
26.3%
6 6517
12.1%
7 5651
 
10.5%
5 4072
 
7.5%
8 3448
 
6.4%
2 1527
 
2.8%
4 1425
 
2.6%
3 1355
 
2.5%
9 1003
 
1.9%

Weight
Text

Distinct323
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size959.7 KiB
2025-04-20T18:20:38.753265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length4
Mean length4.1290799
Min length0

Characters and Unicode

Total characters58699
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique146 ?
Unique (%)1.0%

Sample

1st row63.0
2nd row79.0
3rd row76.0
4th row80.0
5th row56.0
ValueCountFrequency (%)
80.0 579
 
4.1%
70.0 519
 
3.7%
75.0 450
 
3.2%
65.0 380
 
2.7%
85.0 375
 
2.6%
90.0 371
 
2.6%
60.0 363
 
2.6%
72.0 348
 
2.5%
82.0 332
 
2.3%
78.0 331
 
2.3%
Other values (312) 10146
71.5%
2025-04-20T18:20:39.081034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 17303
29.5%
. 14194
24.2%
7 4610
 
7.9%
8 4243
 
7.2%
6 3962
 
6.7%
5 3580
 
6.1%
1 3409
 
5.8%
9 2682
 
4.6%
2 1732
 
3.0%
4 1627
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58699
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 17303
29.5%
. 14194
24.2%
7 4610
 
7.9%
8 4243
 
7.2%
6 3962
 
6.7%
5 3580
 
6.1%
1 3409
 
5.8%
9 2682
 
4.6%
2 1732
 
3.0%
4 1627
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58699
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 17303
29.5%
. 14194
24.2%
7 4610
 
7.9%
8 4243
 
7.2%
6 3962
 
6.7%
5 3580
 
6.1%
1 3409
 
5.8%
9 2682
 
4.6%
2 1732
 
3.0%
4 1627
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58699
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 17303
29.5%
. 14194
24.2%
7 4610
 
7.9%
8 4243
 
7.2%
6 3962
 
6.7%
5 3580
 
6.1%
1 3409
 
5.8%
9 2682
 
4.6%
2 1732
 
3.0%
4 1627
 
2.8%
Distinct85
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.429094
Minimum-4
Maximum100
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size222.1 KiB
2025-04-20T18:20:39.194369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile27
Q144
median57
Q368
95-th percentile78
Maximum100
Range104
Interquartile range (IQR)24

Descriptive statistics

Standard deviation15.60156
Coefficient of variation (CV)0.2814688
Kurtosis-0.53765177
Mean55.429094
Median Absolute Deviation (MAD)11
Skewness-0.36209426
Sum787980
Variance243.40869
MonotonicityNot monotonic
2025-04-20T18:20:39.288134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 438
 
3.1%
66 420
 
3.0%
65 405
 
2.8%
67 393
 
2.8%
68 384
 
2.7%
69 383
 
2.7%
71 377
 
2.7%
64 363
 
2.6%
63 329
 
2.3%
72 325
 
2.3%
Other values (75) 10399
73.1%
ValueCountFrequency (%)
-4 1
 
< 0.1%
13 2
 
< 0.1%
14 1
 
< 0.1%
15 2
 
< 0.1%
16 27
 
0.2%
17 46
0.3%
18 76
0.5%
19 53
0.4%
20 37
0.3%
21 49
0.3%
ValueCountFrequency (%)
100 1
 
< 0.1%
97 1
 
< 0.1%
95 2
 
< 0.1%
93 2
 
< 0.1%
92 2
 
< 0.1%
91 2
 
< 0.1%
90 7
 
< 0.1%
89 9
0.1%
88 22
0.2%
87 21
0.1%
Distinct12687
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
2025-04-20T18:20:39.509181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length688
Median length381
Mean length69.4713
Min length0

Characters and Unicode

Total characters987604
Distinct characters121
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12156 ?
Unique (%)85.5%

Sample

1st rowAsthma PSORIATIC ARTHRITIS, severe arthritis
2nd rowBRCA possitive, Diabetes mellitus Hypercholesterolemia 2005 נונהודג'קין - טופלה בהקרנות 2008 הישנות של למפומה בירך שמאל - טופלה בכימו 2010 הישנות בירך עברה השתלת מח עצם עצמית נשאות של BRCA 2013 MI - יש אישור של קרדיולוג
3rd rowOVARIA CYST, ovarian polycystic חסימה בחצוצרה ימנית
4th rowDepressive disorder POLYCYSTIC OVARIES, נשאית BRCA1
5th rowHypothyroidism Osteoporosis סרטן שד עברה כימו 2017, Lap B.S.O.
ValueCountFrequency (%)
5193
 
4.0%
hypertension 4340
 
3.3%
hypercholesterolemia 2450
 
1.9%
diabetes 2257
 
1.7%
mellitus 2248
 
1.7%
of 1818
 
1.4%
hyperlipidemia 1522
 
1.2%
breast 1333
 
1.0%
hernia 1306
 
1.0%
rt 1286
 
1.0%
Other values (13648) 105936
81.7%
2025-04-20T18:20:39.848895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
134514
 
13.6%
e 49281
 
5.0%
i 37684
 
3.8%
r 30074
 
3.0%
t 29852
 
3.0%
s 29814
 
3.0%
o 27530
 
2.8%
I 25605
 
2.6%
a 25401
 
2.6%
A 23780
 
2.4%
Other values (111) 574069
58.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 987604
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
134514
 
13.6%
e 49281
 
5.0%
i 37684
 
3.8%
r 30074
 
3.0%
t 29852
 
3.0%
s 29814
 
3.0%
o 27530
 
2.8%
I 25605
 
2.6%
a 25401
 
2.6%
A 23780
 
2.4%
Other values (111) 574069
58.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 987604
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
134514
 
13.6%
e 49281
 
5.0%
i 37684
 
3.8%
r 30074
 
3.0%
t 29852
 
3.0%
s 29814
 
3.0%
o 27530
 
2.8%
I 25605
 
2.6%
a 25401
 
2.6%
A 23780
 
2.4%
Other values (111) 574069
58.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 987604
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
134514
 
13.6%
e 49281
 
5.0%
i 37684
 
3.8%
r 30074
 
3.0%
t 29852
 
3.0%
s 29814
 
3.0%
o 27530
 
2.8%
I 25605
 
2.6%
a 25401
 
2.6%
A 23780
 
2.4%
Other values (111) 574069
58.1%
Distinct298
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size222.1 KiB
Minimum2017-01-01 00:00:00
Maximum2017-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-20T18:20:39.937845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:20:40.036778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Planned Surgery Time
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size777.4 KiB
Distinct298
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size222.1 KiB
Minimum2017-01-01 00:00:00
Maximum2017-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-20T18:20:40.137540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:20:40.246004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Administrative Admission Time
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size777.4 KiB

Planned Operating Room Number
Categorical

High correlation 

Distinct28
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size999.6 KiB
20010.0
1319 
20012.0
1268 
20009.0
1177 
20011.0
1175 
20002.0
972 
Other values (23)
8305 

Length

Max length16
Median length7
Mean length7.0040799
Min length6

Characters and Unicode

Total characters99570
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st row20002.0
2nd row20009.0
3rd row20010.0
4th row20003.0
5th row20001.0

Common Values

ValueCountFrequency (%)
20010.0 1319
 
9.3%
20012.0 1268
 
8.9%
20009.0 1177
 
8.3%
20011.0 1175
 
8.3%
20002.0 972
 
6.8%
20006.0 958
 
6.7%
20003.0 939
 
6.6%
20001.0 917
 
6.5%
20007.0 896
 
6.3%
20004.0 877
 
6.2%
Other values (18) 3718
26.2%

Length

2025-04-20T18:20:40.331888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20010.0 1321
 
9.3%
20012.0 1271
 
8.9%
20009.0 1179
 
8.3%
20011.0 1175
 
8.3%
20002.0 973
 
6.8%
20006.0 959
 
6.7%
20003.0 941
 
6.6%
20001.0 919
 
6.5%
20007.0 897
 
6.3%
20004.0 878
 
6.2%
Other values (10) 3713
26.1%

Most occurring characters

ValueCountFrequency (%)
0 51715
51.9%
2 16445
 
16.5%
. 14226
 
14.3%
1 8502
 
8.5%
6 1668
 
1.7%
4 1623
 
1.6%
5 1554
 
1.6%
3 1444
 
1.5%
9 1275
 
1.3%
7 897
 
0.9%
Other values (4) 221
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 99570
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 51715
51.9%
2 16445
 
16.5%
. 14226
 
14.3%
1 8502
 
8.5%
6 1668
 
1.7%
4 1623
 
1.6%
5 1554
 
1.6%
3 1444
 
1.5%
9 1275
 
1.3%
7 897
 
0.9%
Other values (4) 221
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 99570
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 51715
51.9%
2 16445
 
16.5%
. 14226
 
14.3%
1 8502
 
8.5%
6 1668
 
1.7%
4 1623
 
1.6%
5 1554
 
1.6%
3 1444
 
1.5%
9 1275
 
1.3%
7 897
 
0.9%
Other values (4) 221
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 99570
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 51715
51.9%
2 16445
 
16.5%
. 14226
 
14.3%
1 8502
 
8.5%
6 1668
 
1.7%
4 1623
 
1.6%
5 1554
 
1.6%
3 1444
 
1.5%
9 1275
 
1.3%
7 897
 
0.9%
Other values (4) 221
 
0.2%
Distinct270
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size222.1 KiB
Minimum2017-01-01 00:00:00
Maximum2017-12-28 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-20T18:20:40.406673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:20:40.498097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Pre-Surgery Hospitalization Admission Time
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size777.4 KiB

Pre-Surgical Admission Time Before Surgery
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size777.4 KiB
Distinct10367
Distinct (%)72.9%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2025-04-20T18:20:40.715898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length122
Median length106
Mean length58.969119
Min length0

Characters and Unicode

Total characters838305
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8090 ?
Unique (%)56.9%

Sample

1st row126.0, 30817.0, 315.0, 321566226.0, 33535543.0, 33724626.0, 8.0
2nd row
3rd row142.0, 16096.0, 16415572.0, 18796.0, 314291626.0, 57475287.0
4th row101.0, 171.0, 314421264.0, 31448.0, 315.0, 322138181.0
5th row
ValueCountFrequency (%)
8.0 878
 
1.0%
190.0 859
 
1.0%
324757558.0 786
 
0.9%
139.0 745
 
0.9%
203.0 738
 
0.9%
171.0 730
 
0.9%
174.0 719
 
0.9%
303327142.0 712
 
0.8%
40852204.0 698
 
0.8%
306897638.0 662
 
0.8%
Other values (964) 76624
91.1%
2025-04-20T18:20:41.048958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 135732
16.2%
. 84151
10.0%
3 84028
10.0%
1 74884
8.9%
2 70989
8.5%
70167
8.4%
, 70167
8.4%
7 43518
 
5.2%
6 42370
 
5.1%
5 41793
 
5.0%
Other values (3) 120506
14.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 838305
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 135732
16.2%
. 84151
10.0%
3 84028
10.0%
1 74884
8.9%
2 70989
8.5%
70167
8.4%
, 70167
8.4%
7 43518
 
5.2%
6 42370
 
5.1%
5 41793
 
5.0%
Other values (3) 120506
14.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 838305
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 135732
16.2%
. 84151
10.0%
3 84028
10.0%
1 74884
8.9%
2 70989
8.5%
70167
8.4%
, 70167
8.4%
7 43518
 
5.2%
6 42370
 
5.1%
5 41793
 
5.0%
Other values (3) 120506
14.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 838305
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 135732
16.2%
. 84151
10.0%
3 84028
10.0%
1 74884
8.9%
2 70989
8.5%
70167
8.4%
, 70167
8.4%
7 43518
 
5.2%
6 42370
 
5.1%
5 41793
 
5.0%
Other values (3) 120506
14.4%

Anesthesiologist Code
Real number (ℝ)

Distinct73
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2049.7617
Minimum0
Maximum4058
Zeros57
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size222.1 KiB
2025-04-20T18:20:41.149198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1223
Q11679
median2064
Q32284
95-th percentile2972
Maximum4058
Range4058
Interquartile range (IQR)605

Descriptive statistics

Standard deviation512.98355
Coefficient of variation (CV)0.25026497
Kurtosis1.9981135
Mean2049.7617
Median Absolute Deviation (MAD)242
Skewness0.44132268
Sum29139413
Variance263152.13
MonotonicityNot monotonic
2025-04-20T18:20:41.262504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2074 523
 
3.7%
2065 459
 
3.2%
2827 435
 
3.1%
2021 431
 
3.0%
2546 427
 
3.0%
1643 425
 
3.0%
1667 422
 
3.0%
2050 404
 
2.8%
2064 397
 
2.8%
1884 387
 
2.7%
Other values (63) 9906
69.7%
ValueCountFrequency (%)
0 57
 
0.4%
871 17
 
0.1%
887 64
 
0.5%
1172 81
 
0.6%
1182 4
 
< 0.1%
1219 348
2.4%
1223 279
2.0%
1382 24
 
0.2%
1389 259
1.8%
1407 326
2.3%
ValueCountFrequency (%)
4058 3
 
< 0.1%
3739 140
 
1.0%
3590 170
 
1.2%
3389 4
 
< 0.1%
3249 329
2.3%
2972 119
 
0.8%
2827 435
3.1%
2705 1
 
< 0.1%
2622 4
 
< 0.1%
2581 86
 
0.6%

Anesthesia Code
Categorical

High correlation  Imbalance 

Distinct45
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size955.2 KiB
1.0
11592 
4.0
 
835
13.0, 4.0
 
630
1.0, 12.0
 
494
1.0, 18.0
 
139
Other values (40)
 
526

Length

Max length21
Median length3
Mean length3.8061339
Min length0

Characters and Unicode

Total characters54108
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.1%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 11592
81.5%
4.0 835
 
5.9%
13.0, 4.0 630
 
4.4%
1.0, 12.0 494
 
3.5%
1.0, 18.0 139
 
1.0%
13.0, 18.0 124
 
0.9%
12.0, 13.0, 4.0 98
 
0.7%
1.0, 4.0 75
 
0.5%
13.0 34
 
0.2%
18.0 20
 
0.1%
Other values (35) 175
 
1.2%

Length

2025-04-20T18:20:41.358192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1.0 12366
76.8%
4.0 1717
 
10.7%
13.0 970
 
6.0%
12.0 654
 
4.1%
18.0 326
 
2.0%
17.0 41
 
0.3%
3.0 27
 
0.2%
20.0 4
 
< 0.1%
31.0 1
 
< 0.1%
16.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 16112
29.8%
. 16108
29.8%
1 14359
26.5%
1893
 
3.5%
, 1893
 
3.5%
4 1717
 
3.2%
3 998
 
1.8%
2 659
 
1.2%
8 326
 
0.6%
7 42
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54108
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 16112
29.8%
. 16108
29.8%
1 14359
26.5%
1893
 
3.5%
, 1893
 
3.5%
4 1717
 
3.2%
3 998
 
1.8%
2 659
 
1.2%
8 326
 
0.6%
7 42
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54108
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 16112
29.8%
. 16108
29.8%
1 14359
26.5%
1893
 
3.5%
, 1893
 
3.5%
4 1717
 
3.2%
3 998
 
1.8%
2 659
 
1.2%
8 326
 
0.6%
7 42
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54108
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 16112
29.8%
. 16108
29.8%
1 14359
26.5%
1893
 
3.5%
, 1893
 
3.5%
4 1717
 
3.2%
3 998
 
1.8%
2 659
 
1.2%
8 326
 
0.6%
7 42
 
0.1%

Type of Anesthesia
Categorical

High correlation  Imbalance 

Distinct45
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1013.5 KiB
General
11592 
Spinal
 
835
Sedation, Spinal
 
630
Block, General
 
494
General, Local
 
139
Other values (40)
 
526

Length

Max length34
Median length7
Mean length8.0021103
Min length0

Characters and Unicode

Total characters113758
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.1%

Sample

1st rowGeneral
2nd rowGeneral
3rd rowGeneral
4th rowGeneral
5th rowGeneral

Common Values

ValueCountFrequency (%)
General 11592
81.5%
Spinal 835
 
5.9%
Sedation, Spinal 630
 
4.4%
Block, General 494
 
3.5%
General, Local 139
 
1.0%
Local, Sedation 124
 
0.9%
Block, Sedation, Spinal 98
 
0.7%
General, Spinal 75
 
0.5%
Sedation 34
 
0.2%
Local 20
 
0.1%
Other values (35) 175
 
1.2%

Length

2025-04-20T18:20:41.453037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
general 12366
76.7%
spinal 1717
 
10.7%
sedation 971
 
6.0%
block 654
 
4.1%
local 326
 
2.0%
mac 41
 
0.3%
epidural 27
 
0.2%
without 4
 
< 0.1%
anesthesia 4
 
< 0.1%
sub 1
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 25712
22.6%
a 15419
13.6%
l 15092
13.3%
n 15059
13.2%
r 12393
10.9%
G 12366
10.9%
i 2724
 
2.4%
S 2689
 
2.4%
o 1957
 
1.7%
1899
 
1.7%
Other values (20) 8448
 
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113758
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 25712
22.6%
a 15419
13.6%
l 15092
13.3%
n 15059
13.2%
r 12393
10.9%
G 12366
10.9%
i 2724
 
2.4%
S 2689
 
2.4%
o 1957
 
1.7%
1899
 
1.7%
Other values (20) 8448
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113758
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 25712
22.6%
a 15419
13.6%
l 15092
13.3%
n 15059
13.2%
r 12393
10.9%
G 12366
10.9%
i 2724
 
2.4%
S 2689
 
2.4%
o 1957
 
1.7%
1899
 
1.7%
Other values (20) 8448
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113758
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 25712
22.6%
a 15419
13.6%
l 15092
13.3%
n 15059
13.2%
r 12393
10.9%
G 12366
10.9%
i 2724
 
2.4%
S 2689
 
2.4%
o 1957
 
1.7%
1899
 
1.7%
Other values (20) 8448
 
7.4%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size902.4 KiB
14216 

Length

Max length0
Median length0
Mean length0
Min length0

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
14216
100.0%

Length

2025-04-20T18:20:41.536875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T18:20:41.578353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Actual Operating Room Number
Categorical

High correlation 

Distinct18
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size971.8 KiB
20010
1289 
20012
1266 
20009
1179 
20011
1151 
20002
966 
Other values (13)
8365 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters71080
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row20002
2nd row20009
3rd row20014
4th row20002
5th row20002

Common Values

ValueCountFrequency (%)
20010 1289
 
9.1%
20012 1266
 
8.9%
20009 1179
 
8.3%
20011 1151
 
8.1%
20002 966
 
6.8%
20001 958
 
6.7%
20003 944
 
6.6%
20006 931
 
6.5%
20007 891
 
6.3%
20005 861
 
6.1%
Other values (8) 3780
26.6%

Length

2025-04-20T18:20:41.626805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20010 1289
 
9.1%
20012 1266
 
8.9%
20009 1179
 
8.3%
20011 1151
 
8.1%
20002 966
 
6.8%
20001 958
 
6.7%
20003 944
 
6.6%
20006 931
 
6.5%
20007 891
 
6.3%
20005 861
 
6.1%
Other values (8) 3780
26.6%

Most occurring characters

ValueCountFrequency (%)
0 37482
52.7%
2 16455
23.1%
1 8557
 
12.0%
6 1701
 
2.4%
4 1613
 
2.3%
5 1559
 
2.2%
3 1472
 
2.1%
9 1179
 
1.7%
7 891
 
1.3%
8 171
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 71080
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 37482
52.7%
2 16455
23.1%
1 8557
 
12.0%
6 1701
 
2.4%
4 1613
 
2.3%
5 1559
 
2.2%
3 1472
 
2.1%
9 1179
 
1.7%
7 891
 
1.3%
8 171
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 71080
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 37482
52.7%
2 16455
23.1%
1 8557
 
12.0%
6 1701
 
2.4%
4 1613
 
2.3%
5 1559
 
2.2%
3 1472
 
2.1%
9 1179
 
1.7%
7 891
 
1.3%
8 171
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 71080
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 37482
52.7%
2 16455
23.1%
1 8557
 
12.0%
6 1701
 
2.4%
4 1613
 
2.3%
5 1559
 
2.2%
3 1472
 
2.1%
9 1179
 
1.7%
7 891
 
1.3%
8 171
 
0.2%
Distinct298
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size222.1 KiB
Minimum2017-01-01 00:00:00
Maximum2017-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-20T18:20:41.708796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:20:41.812889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Actual Surgery Room Entry Time
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size777.4 KiB

Incision Time
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size777.4 KiB

Closure Time
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size777.4 KiB
Distinct301
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size222.1 KiB
Minimum2017-01-01 00:00:00
Maximum2017-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-20T18:20:41.909819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:20:42.013637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

End of Surgery Time (Exit from OR)
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size777.4 KiB
Distinct290
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size963.4 KiB
2025-04-20T18:20:42.302373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length4
Mean length4.3952589
Min length0

Characters and Unicode

Total characters62483
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)0.3%

Sample

1st row114.0
2nd row45.0
3rd row110.0
4th row46.0
5th row63.0
ValueCountFrequency (%)
120.0 339
 
2.4%
60.0 268
 
1.9%
52.0 251
 
1.8%
85.0 242
 
1.7%
73.0 229
 
1.6%
64.0 228
 
1.6%
55.0 225
 
1.6%
74.0 216
 
1.5%
66.0 205
 
1.4%
80.0 200
 
1.4%
Other values (278) 11773
83.0%
2025-04-20T18:20:42.655581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 17579
28.1%
. 14176
22.7%
1 7629
12.2%
5 4201
 
6.7%
6 3114
 
5.0%
4 3014
 
4.8%
7 2881
 
4.6%
8 2615
 
4.2%
2 2580
 
4.1%
9 2466
 
3.9%
Other values (3) 2228
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 62483
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 17579
28.1%
. 14176
22.7%
1 7629
12.2%
5 4201
 
6.7%
6 3114
 
5.0%
4 3014
 
4.8%
7 2881
 
4.6%
8 2615
 
4.2%
2 2580
 
4.1%
9 2466
 
3.9%
Other values (3) 2228
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 62483
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 17579
28.1%
. 14176
22.7%
1 7629
12.2%
5 4201
 
6.7%
6 3114
 
5.0%
4 3014
 
4.8%
7 2881
 
4.6%
8 2615
 
4.2%
2 2580
 
4.1%
9 2466
 
3.9%
Other values (3) 2228
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 62483
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 17579
28.1%
. 14176
22.7%
1 7629
12.2%
5 4201
 
6.7%
6 3114
 
5.0%
4 3014
 
4.8%
7 2881
 
4.6%
8 2615
 
4.2%
2 2580
 
4.1%
9 2466
 
3.9%
Other values (3) 2228
 
3.6%
Distinct299
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size222.1 KiB
Minimum2017-01-01 00:00:00
Maximum2017-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-20T18:20:42.754659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:20:42.856471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Recovery Room Entry Time
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size777.4 KiB
Distinct304
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size222.1 KiB
Minimum2017-01-01 00:00:00
Maximum2017-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-20T18:20:42.957335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:20:43.067577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Recovery Room Exit Time
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size777.4 KiB
Distinct363
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size222.1 KiB
Minimum2017-01-01 00:00:00
Maximum2017-12-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-20T18:20:43.168203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:20:43.270314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Post-Surgery Discharge Time
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size777.4 KiB
Distinct298
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size222.1 KiB
Minimum2017-01-01 00:00:00
Maximum2017-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-20T18:20:43.368346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:20:43.468750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Planned Start Time for Doctor Block
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size777.4 KiB
Distinct298
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size222.1 KiB
Minimum2017-01-01 00:00:00
Maximum2017-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-20T18:20:43.560793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:20:43.664776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Planned End Time for Doctor Block
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size777.4 KiB

Readmission
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size916.3 KiB
0
14216 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14216
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14216
100.0%

Length

2025-04-20T18:20:43.756504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T18:20:43.801644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 14216
100.0%

Most occurring characters

ValueCountFrequency (%)
0 14216
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 14216
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 14216
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 14216
100.0%

strict_signature
Text

Unique 

Distinct14216
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2025-04-20T18:20:43.989501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length108
Median length105
Mean length104.94021
Min length103

Characters and Unicode

Total characters1491830
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14216 ?
Unique (%)100.0%

Sample

1st row1.57_63.0_60.0_20002_2017-06-18_11:00:46_13:14:00_11:18:00_12:55:00_13:14:00_14:21:00_2017-06-21_12:49:00
2nd row1.7_79.0_64.0_20009_2017-06-28_20:11:08_22:32:00_20:21:00_22:11:00_22:32:00_23:58:00_2017-06-29_18:09:00
3rd row1.65_76.0_25.0_20014_2017-06-18_08:56:12_12:33:00_11:40:00_12:24:00_12:33:00_14:31:00_2017-06-19_16:20:00
4th row1.65_80.0_39.0_20002_2017-06-18_14:38:54_15:35:00_14:51:00_15:22:00_15:35:00_17:03:00_2017-06-19_10:10:00
5th row1.65_56.0_49.0_20002_2017-09-14_16:22:00_17:30:00_16:40:00_17:10:00_17:34:00_18:29:00_2017-09-15_13:24:00
ValueCountFrequency (%)
1.73_69.0_44.0_20007_2017-04-12_07:04:00_09:32:00_07:24:00_09:15:00_09:35:00_10:23:00_2017-04-13_11:50:00 1
 
< 0.1%
1.86_94.0_68.0_20002_2017-08-07_20:43:00_22:26:00_21:15:00_22:27:00_22:33:00_00:08:00_2017-08-08_12:47:00 1
 
< 0.1%
1.57_63.0_60.0_20002_2017-06-18_11:00:46_13:14:00_11:18:00_12:55:00_13:14:00_14:21:00_2017-06-21_12:49:00 1
 
< 0.1%
1.7_79.0_64.0_20009_2017-06-28_20:11:08_22:32:00_20:21:00_22:11:00_22:32:00_23:58:00_2017-06-29_18:09:00 1
 
< 0.1%
1.65_76.0_25.0_20014_2017-06-18_08:56:12_12:33:00_11:40:00_12:24:00_12:33:00_14:31:00_2017-06-19_16:20:00 1
 
< 0.1%
1.65_80.0_39.0_20002_2017-06-18_14:38:54_15:35:00_14:51:00_15:22:00_15:35:00_17:03:00_2017-06-19_10:10:00 1
 
< 0.1%
1.65_56.0_49.0_20002_2017-09-14_16:22:00_17:30:00_16:40:00_17:10:00_17:34:00_18:29:00_2017-09-15_13:24:00 1
 
< 0.1%
1.68_95.0_59.0_20014_2017-03-10_09:15:00_10:41:00_09:39:00_10:29:00_10:46:00_12:32:00_2017-03-11_11:22:00 1
 
< 0.1%
1.75_105.0_55.0_20011_2017-12-31_17:29:00_18:44:00_17:50:00_18:45:00_18:49:00_20:03:00_2017-12-25_17:57:00 1
 
< 0.1%
1.75_78.0_42.0_20014_2017-12-31_12:05:00_13:19:00_12:25:00_13:19:00_13:21:00_14:35:00_2017-12-21_08:14:00 1
 
< 0.1%
Other values (14206) 14206
99.9%
2025-04-20T18:20:44.264858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 389083
26.1%
: 199024
13.3%
1 187737
12.6%
_ 170592
11.4%
2 123108
 
8.3%
7 68452
 
4.6%
- 56865
 
3.8%
5 51136
 
3.4%
3 49354
 
3.3%
4 45563
 
3.1%
Other values (6) 150916
 
10.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1491830
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 389083
26.1%
: 199024
13.3%
1 187737
12.6%
_ 170592
11.4%
2 123108
 
8.3%
7 68452
 
4.6%
- 56865
 
3.8%
5 51136
 
3.4%
3 49354
 
3.3%
4 45563
 
3.1%
Other values (6) 150916
 
10.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1491830
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 389083
26.1%
: 199024
13.3%
1 187737
12.6%
_ 170592
11.4%
2 123108
 
8.3%
7 68452
 
4.6%
- 56865
 
3.8%
5 51136
 
3.4%
3 49354
 
3.3%
4 45563
 
3.1%
Other values (6) 150916
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1491830
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 389083
26.1%
: 199024
13.3%
1 187737
12.6%
_ 170592
11.4%
2 123108
 
8.3%
7 68452
 
4.6%
- 56865
 
3.8%
5 51136
 
3.4%
3 49354
 
3.3%
4 45563
 
3.1%
Other values (6) 150916
 
10.1%

No Show
Boolean

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size124.9 KiB
False
14216 
ValueCountFrequency (%)
False 14216
100.0%
2025-04-20T18:20:44.319229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Planned Surgery Time_Minutes
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size125.2 KiB
4
3895 
1
3514 
2
3474 
3
3333 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14216
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row4
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
4 3895
27.4%
1 3514
24.7%
2 3474
24.4%
3 3333
23.4%

Length

2025-04-20T18:20:44.371516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T18:20:44.424795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 3895
27.4%
1 3514
24.7%
2 3474
24.4%
3 3333
23.4%

Most occurring characters

ValueCountFrequency (%)
4 3895
27.4%
1 3514
24.7%
2 3474
24.4%
3 3333
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 3895
27.4%
1 3514
24.7%
2 3474
24.4%
3 3333
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 3895
27.4%
1 3514
24.7%
2 3474
24.4%
3 3333
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 3895
27.4%
1 3514
24.7%
2 3474
24.4%
3 3333
23.4%

Administrative Admission Time_Minutes
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size125.2 KiB
4
4209 
3
3514 
1
3425 
2
3068 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14216
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row1
4th row3
5th row3

Common Values

ValueCountFrequency (%)
4 4209
29.6%
3 3514
24.7%
1 3425
24.1%
2 3068
21.6%

Length

2025-04-20T18:20:44.499722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T18:20:44.553171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 4209
29.6%
3 3514
24.7%
1 3425
24.1%
2 3068
21.6%

Most occurring characters

ValueCountFrequency (%)
4 4209
29.6%
3 3514
24.7%
1 3425
24.1%
2 3068
21.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 4209
29.6%
3 3514
24.7%
1 3425
24.1%
2 3068
21.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 4209
29.6%
3 3514
24.7%
1 3425
24.1%
2 3068
21.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 4209
29.6%
3 3514
24.7%
1 3425
24.1%
2 3068
21.6%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size125.2 KiB
4
5109 
2
3671 
3
3261 
1
2175 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14216
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row3
4th row1
5th row2

Common Values

ValueCountFrequency (%)
4 5109
35.9%
2 3671
25.8%
3 3261
22.9%
1 2175
15.3%

Length

2025-04-20T18:20:44.629459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T18:20:44.691656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 5109
35.9%
2 3671
25.8%
3 3261
22.9%
1 2175
15.3%

Most occurring characters

ValueCountFrequency (%)
4 5109
35.9%
2 3671
25.8%
3 3261
22.9%
1 2175
15.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 5109
35.9%
2 3671
25.8%
3 3261
22.9%
1 2175
15.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 5109
35.9%
2 3671
25.8%
3 3261
22.9%
1 2175
15.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 5109
35.9%
2 3671
25.8%
3 3261
22.9%
1 2175
15.3%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size125.2 KiB
1
4343 
4
3343 
3
3295 
2
3235 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14216
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row4
3rd row1
4th row2
5th row3

Common Values

ValueCountFrequency (%)
1 4343
30.6%
4 3343
23.5%
3 3295
23.2%
2 3235
22.8%

Length

2025-04-20T18:20:44.769541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T18:20:44.821090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 4343
30.6%
4 3343
23.5%
3 3295
23.2%
2 3235
22.8%

Most occurring characters

ValueCountFrequency (%)
1 4343
30.6%
4 3343
23.5%
3 3295
23.2%
2 3235
22.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 4343
30.6%
4 3343
23.5%
3 3295
23.2%
2 3235
22.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 4343
30.6%
4 3343
23.5%
3 3295
23.2%
2 3235
22.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 4343
30.6%
4 3343
23.5%
3 3295
23.2%
2 3235
22.8%

Actual Surgery Room Entry Time_Minutes
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size125.2 KiB
4
3952 
1
3557 
3
3441 
2
3266 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14216
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row4
3rd row1
4th row3
5th row3

Common Values

ValueCountFrequency (%)
4 3952
27.8%
1 3557
25.0%
3 3441
24.2%
2 3266
23.0%

Length

2025-04-20T18:20:44.895312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T18:20:44.947247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 3952
27.8%
1 3557
25.0%
3 3441
24.2%
2 3266
23.0%

Most occurring characters

ValueCountFrequency (%)
4 3952
27.8%
1 3557
25.0%
3 3441
24.2%
2 3266
23.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 3952
27.8%
1 3557
25.0%
3 3441
24.2%
2 3266
23.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 3952
27.8%
1 3557
25.0%
3 3441
24.2%
2 3266
23.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 3952
27.8%
1 3557
25.0%
3 3441
24.2%
2 3266
23.0%

Incision Time_Minutes
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size125.2 KiB
4
4002 
1
3462 
2
3409 
3
3343 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14216
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row4
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
4 4002
28.2%
1 3462
24.4%
2 3409
24.0%
3 3343
23.5%

Length

2025-04-20T18:20:45.021379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T18:20:45.077473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 4002
28.2%
1 3462
24.4%
2 3409
24.0%
3 3343
23.5%

Most occurring characters

ValueCountFrequency (%)
4 4002
28.2%
1 3462
24.4%
2 3409
24.0%
3 3343
23.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 4002
28.2%
1 3462
24.4%
2 3409
24.0%
3 3343
23.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 4002
28.2%
1 3462
24.4%
2 3409
24.0%
3 3343
23.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 4002
28.2%
1 3462
24.4%
2 3409
24.0%
3 3343
23.5%

Closure Time_Minutes
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size125.2 KiB
4
4206 
2
3470 
3
3332 
1
3208 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14216
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row4
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
4 4206
29.6%
2 3470
24.4%
3 3332
23.4%
1 3208
22.6%

Length

2025-04-20T18:20:45.154564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T18:20:45.210453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 4206
29.6%
2 3470
24.4%
3 3332
23.4%
1 3208
22.6%

Most occurring characters

ValueCountFrequency (%)
4 4206
29.6%
2 3470
24.4%
3 3332
23.4%
1 3208
22.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 4206
29.6%
2 3470
24.4%
3 3332
23.4%
1 3208
22.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 4206
29.6%
2 3470
24.4%
3 3332
23.4%
1 3208
22.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 4206
29.6%
2 3470
24.4%
3 3332
23.4%
1 3208
22.6%

End of Surgery Time (Exit from OR)_Minutes
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size125.2 KiB
4
4184 
3
3499 
2
3328 
1
3205 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14216
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row4
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
4 4184
29.4%
3 3499
24.6%
2 3328
23.4%
1 3205
22.5%

Length

2025-04-20T18:20:45.287059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T18:20:45.354649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 4184
29.4%
3 3499
24.6%
2 3328
23.4%
1 3205
22.5%

Most occurring characters

ValueCountFrequency (%)
4 4184
29.4%
3 3499
24.6%
2 3328
23.4%
1 3205
22.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 4184
29.4%
3 3499
24.6%
2 3328
23.4%
1 3205
22.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 4184
29.4%
3 3499
24.6%
2 3328
23.4%
1 3205
22.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 4184
29.4%
3 3499
24.6%
2 3328
23.4%
1 3205
22.5%

Recovery Room Entry Time_Minutes
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size125.2 KiB
4
4188 
3
3624 
2
3378 
1
3026 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14216
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row4
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
4 4188
29.5%
3 3624
25.5%
2 3378
23.8%
1 3026
21.3%

Length

2025-04-20T18:20:45.433522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T18:20:45.485758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 4188
29.5%
3 3624
25.5%
2 3378
23.8%
1 3026
21.3%

Most occurring characters

ValueCountFrequency (%)
4 4188
29.5%
3 3624
25.5%
2 3378
23.8%
1 3026
21.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 4188
29.5%
3 3624
25.5%
2 3378
23.8%
1 3026
21.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 4188
29.5%
3 3624
25.5%
2 3378
23.8%
1 3026
21.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 4188
29.5%
3 3624
25.5%
2 3378
23.8%
1 3026
21.3%

Recovery Room Exit Time_Minutes
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size125.2 KiB
4
4121 
3
3601 
2
3355 
1
3139 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14216
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row4
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
4 4121
29.0%
3 3601
25.3%
2 3355
23.6%
1 3139
22.1%

Length

2025-04-20T18:20:45.557935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T18:20:45.621851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 4121
29.0%
3 3601
25.3%
2 3355
23.6%
1 3139
22.1%

Most occurring characters

ValueCountFrequency (%)
4 4121
29.0%
3 3601
25.3%
2 3355
23.6%
1 3139
22.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 4121
29.0%
3 3601
25.3%
2 3355
23.6%
1 3139
22.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 4121
29.0%
3 3601
25.3%
2 3355
23.6%
1 3139
22.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 4121
29.0%
3 3601
25.3%
2 3355
23.6%
1 3139
22.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size125.2 KiB
2
4201 
3
3888 
1
3519 
4
2608 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14216
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row4
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
2 4201
29.6%
3 3888
27.3%
1 3519
24.8%
4 2608
18.3%

Length

2025-04-20T18:20:45.706692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T18:20:45.761685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 4201
29.6%
3 3888
27.3%
1 3519
24.8%
4 2608
18.3%

Most occurring characters

ValueCountFrequency (%)
2 4201
29.6%
3 3888
27.3%
1 3519
24.8%
4 2608
18.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 4201
29.6%
3 3888
27.3%
1 3519
24.8%
4 2608
18.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 4201
29.6%
3 3888
27.3%
1 3519
24.8%
4 2608
18.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 4201
29.6%
3 3888
27.3%
1 3519
24.8%
4 2608
18.3%

Planned Start Time for Doctor Block_Minutes
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size125.2 KiB
1
3882 
3
3882 
4
3633 
2
2819 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14216
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row2
4th row3
5th row4

Common Values

ValueCountFrequency (%)
1 3882
27.3%
3 3882
27.3%
4 3633
25.6%
2 2819
19.8%

Length

2025-04-20T18:20:45.840030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T18:20:45.908136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 3882
27.3%
3 3882
27.3%
4 3633
25.6%
2 2819
19.8%

Most occurring characters

ValueCountFrequency (%)
1 3882
27.3%
3 3882
27.3%
4 3633
25.6%
2 2819
19.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3882
27.3%
3 3882
27.3%
4 3633
25.6%
2 2819
19.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3882
27.3%
3 3882
27.3%
4 3633
25.6%
2 2819
19.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3882
27.3%
3 3882
27.3%
4 3633
25.6%
2 2819
19.8%

Planned End Time for Doctor Block_Minutes
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size125.2 KiB
4
3830 
3
3645 
2
3418 
1
3323 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14216
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row1
4th row2
5th row3

Common Values

ValueCountFrequency (%)
4 3830
26.9%
3 3645
25.6%
2 3418
24.0%
1 3323
23.4%

Length

2025-04-20T18:20:45.988972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T18:20:46.080873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 3830
26.9%
3 3645
25.6%
2 3418
24.0%
1 3323
23.4%

Most occurring characters

ValueCountFrequency (%)
4 3830
26.9%
3 3645
25.6%
2 3418
24.0%
1 3323
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 3830
26.9%
3 3645
25.6%
2 3418
24.0%
1 3323
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 3830
26.9%
3 3645
25.6%
2 3418
24.0%
1 3323
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14216
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 3830
26.9%
3 3645
25.6%
2 3418
24.0%
1 3323
23.4%

Interactions

2025-04-20T18:20:14.430356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:17:47.078192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:18:02.138581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:18:21.751950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:19:59.463969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:20:14.660758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:17:47.152274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:18:02.966229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:18:39.684249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:19:59.716823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:20:15.664618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:17:47.988347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:18:04.504713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:18:58.328039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:20:00.961309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:20:34.221725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:18:01.632175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:18:19.782543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:19:25.227585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:20:13.619738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:20:34.749948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:18:01.894523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:18:20.746072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:19:41.904132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-20T18:20:14.025862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-20T18:20:46.222301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Activity CodeActivity Type CodeActual Operating Room NumberActual Surgery Room Entry Time_MinutesAdministrative Admission Time_MinutesAnesthesia CodeAnesthesiologist CodeClosure Time_MinutesEnd of Surgery Time (Exit from OR)_MinutesIncision Time_MinutesPatient Age (on Surgery Day)Planned End Time for Doctor Block_MinutesPlanned Operating Room NumberPlanned SU Time (Large/Medium/Small)Planned Start Time for Doctor Block_MinutesPlanned Surgery Time_MinutesPost-Surgery Discharge Time_MinutesPre-Surgery Hospitalization Admission Time_MinutesPre-Surgery Patient File (External)Pre-Surgical Admission Time Before Surgery_MinutesRecovery Room Entry Time_MinutesRecovery Room Exit Time_MinutesType of Anesthesiapatient_id
Activity Code1.0000.9880.3060.2260.2410.339-0.0360.2280.2290.2260.1550.2360.2300.0710.2740.2250.2250.1870.0100.2320.2290.1930.3390.015
Activity Type Code0.9881.0000.2290.1120.1120.3440.2640.0980.0960.1070.1110.1110.2320.0520.1570.1040.1320.0960.0290.1040.0980.0800.3440.178
Actual Operating Room Number0.3060.2291.0000.0770.0850.1140.1930.0640.0650.0750.0390.0920.8360.0450.1180.0760.0740.0680.0350.0790.0650.0640.1140.122
Actual Surgery Room Entry Time_Minutes0.2260.1120.0771.0000.6580.0870.2270.8290.8260.9640.0310.5490.0740.1270.5540.8140.0530.0360.0260.7410.8240.6140.0870.181
Administrative Admission Time_Minutes0.2410.1120.0850.6581.0000.0740.2050.5960.5950.6550.0270.4650.0840.0870.4780.6370.0450.0410.0280.6880.5890.4800.0740.154
Anesthesia Code0.3390.3440.1140.0870.0741.0000.1450.0830.0830.0820.0610.0950.0850.0250.0980.0830.0670.0480.0560.0680.0820.0801.0000.116
Anesthesiologist Code-0.0360.2640.1930.2270.2050.1451.0000.2210.2230.228-0.0270.2380.1490.0420.2570.2260.0770.0750.0400.2330.2210.1930.1450.028
Closure Time_Minutes0.2280.0980.0640.8290.5960.0830.2211.0000.9480.8490.0360.5310.0650.1080.5390.7410.0450.0350.0260.6860.9310.6620.0830.176
End of Surgery Time (Exit from OR)_Minutes0.2290.0960.0650.8260.5950.0830.2230.9481.0000.8450.0370.5310.0660.1030.5400.7380.0440.0380.0260.6840.9510.6770.0830.175
Incision Time_Minutes0.2260.1070.0750.9640.6550.0820.2280.8490.8451.0000.0310.5520.0720.1240.5540.8140.0520.0340.0260.7430.8420.6210.0820.182
Patient Age (on Surgery Day)0.1550.1110.0390.0310.0270.061-0.0270.0360.0370.0311.0000.0330.0120.0000.0300.0400.0530.109-0.0010.0230.0370.0260.061-0.041
Planned End Time for Doctor Block_Minutes0.2360.1110.0920.5490.4650.0950.2380.5310.5310.5520.0331.0000.0930.1120.4960.5670.0400.0350.0260.5510.5290.4450.0950.218
Planned Operating Room Number0.2300.2320.8360.0740.0840.0850.1490.0650.0660.0720.0120.0931.0000.0440.1200.0880.0730.0680.0440.0770.0660.0610.0850.121
Planned SU Time (Large/Medium/Small)0.0710.0520.0450.1270.0870.0250.0420.1080.1030.1240.0000.1120.0441.0000.0910.1210.0230.0330.0380.1220.0980.1300.0250.053
Planned Start Time for Doctor Block_Minutes0.2740.1570.1180.5540.4780.0980.2570.5390.5400.5540.0300.4960.1200.0911.0000.5640.0450.0270.0260.5390.5420.4590.0980.205
Planned Surgery Time_Minutes0.2250.1040.0760.8140.6370.0830.2260.7410.7380.8140.0400.5670.0880.1210.5641.0000.0540.0360.0260.7100.7360.5720.0830.184
Post-Surgery Discharge Time_Minutes0.2250.1320.0740.0530.0450.0670.0770.0450.0440.0520.0530.0400.0730.0230.0450.0541.0000.0280.0280.0510.0450.0410.0670.068
Pre-Surgery Hospitalization Admission Time_Minutes0.1870.0960.0680.0360.0410.0480.0750.0350.0380.0340.1090.0350.0680.0330.0270.0360.0281.0000.0280.0410.0350.0260.0480.039
Pre-Surgery Patient File (External)0.0100.0290.0350.0260.0280.0560.0400.0260.0260.026-0.0010.0260.0440.0380.0260.0260.0280.0281.0000.0280.0280.0280.0560.032
Pre-Surgical Admission Time Before Surgery_Minutes0.2320.1040.0790.7410.6880.0680.2330.6860.6840.7430.0230.5510.0770.1220.5390.7100.0510.0410.0281.0000.6790.5470.0680.191
Recovery Room Entry Time_Minutes0.2290.0980.0650.8240.5890.0820.2210.9310.9510.8420.0370.5290.0660.0980.5420.7360.0450.0350.0280.6791.0000.7010.0820.175
Recovery Room Exit Time_Minutes0.1930.0800.0640.6140.4800.0800.1930.6620.6770.6210.0260.4450.0610.1300.4590.5720.0410.0260.0280.5470.7011.0000.0800.139
Type of Anesthesia0.3390.3440.1140.0870.0741.0000.1450.0830.0830.0820.0610.0950.0850.0250.0980.0830.0670.0480.0560.0680.0820.0801.0000.116
patient_id0.0150.1780.1220.1810.1540.1160.0280.1760.1750.182-0.0410.2180.1210.0530.2050.1840.0680.0390.0320.1910.1750.1390.1161.000

Missing values

2025-04-20T18:20:35.552623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-20T18:20:36.197080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

patient_idSite CodeMain Surgeon CodeActivity CodeActivity Type CodePlanned SU Time (Large/Medium/Small)Pre-Surgery Patient File (External)Pre-Surgery Admission DateHeightWeightPatient Age (on Surgery Day)Background Diseases/DiagnosesPlanned Surgery DatePlanned Surgery TimeSurgery Admission DateAdministrative Admission TimePlanned Operating Room NumberPre-Surgery Hospitalization Admission DatePre-Surgery Hospitalization Admission TimePre-Surgical Admission Time Before SurgerySurgical Team CodesAnesthesiologist CodeAnesthesia CodeType of AnesthesiaCancellation Reason on Surgery DayActual Operating Room NumberActual Surgery Room Entry DateActual Surgery Room Entry TimeIncision TimeClosure TimeEnd of Surgery Date (Exit from OR)End of Surgery Time (Exit from OR)Planned Surgery DurationRecovery Room Entry DateRecovery Room Entry TimeRecovery Room Exit DateRecovery Room Exit TimePost-Surgery Discharge DatePost-Surgery Discharge TimePlanned Start Date for Doctor BlockPlanned Start Time for Doctor BlockPlanned End Date for Doctor BlockPlanned End Time for Doctor BlockReadmissionstrict_signatureNo ShowPlanned Surgery Time_MinutesAdministrative Admission Time_MinutesPre-Surgery Hospitalization Admission Time_MinutesPre-Surgical Admission Time Before Surgery_MinutesActual Surgery Room Entry Time_MinutesIncision Time_MinutesClosure Time_MinutesEnd of Surgery Time (Exit from OR)_MinutesRecovery Room Entry Time_MinutesRecovery Room Exit Time_MinutesPost-Surgery Discharge Time_MinutesPlanned Start Time for Doctor Block_MinutesPlanned End Time for Doctor Block_Minutes
0120-999, 3081781.511520.023457403.02017-06-081.5763.060.0Asthma PSORIATIC ARTHRITIS, severe arthritis2017-06-1810:50:002017-06-1807:30:0420002.02017-06-0809:45:0010:07:00126.0, 30817.0, 315.0, 321566226.0, 33535543.0, 33724626.0, 8.02064.01.0General200022017-06-1811:00:4611:18:0012:55:002017-06-1813:14:00114.02017-06-1813:14:002017-06-1814:21:002017-06-2112:49:002017-06-1807:00:002017-06-1812:44:0001.57_63.0_60.0_20002_2017-06-18_11:00:46_13:14:00_11:18:00_12:55:00_13:14:00_14:21:00_2017-06-21_12:49:00False2122222222211
1220-999, 1873165.212220.023457802.02017-06-151.779.064.0BRCA possitive, Diabetes mellitus Hypercholesterolemia 2005 נונהודג'קין - טופלה בהקרנות 2008 הישנות של למפומה בירך שמאל - טופלה בכימו 2010 הישנות בירך עברה השתלת מח עצם עצמית נשאות של BRCA 2013 MI - יש אישור של קרדיולוג2017-06-2820:10:002017-06-2817:55:4820009.02017-06-1511:59:0018:25:001417.01.0General200092017-06-2820:11:0820:21:0022:11:002017-06-2822:32:0045.02017-06-2822:32:002017-06-2823:58:002017-06-2918:09:002017-06-2815:00:002017-06-2820:55:0001.7_79.0_64.0_20009_2017-06-28_20:11:08_22:32:00_20:21:00_22:11:00_22:32:00_23:58:00_2017-06-29_18:09:00False4424444444433
6720-999, 1879665.21220.023457800.02017-06-151.6576.025.0OVARIA CYST, ovarian polycystic חסימה בחצוצרה ימנית2017-06-1810:40:002017-06-1807:39:2020010.02017-06-1514:25:0008:11:00142.0, 16096.0, 16415572.0, 18796.0, 314291626.0, 57475287.01223.01.0General200142017-06-1808:56:1211:40:0012:24:002017-06-1812:33:00110.02017-06-1812:33:002017-06-1814:31:002017-06-1916:20:002017-06-1810:40:002017-06-1812:30:0001.65_76.0_25.0_20014_2017-06-18_08:56:12_12:33:00_11:40:00_12:24:00_12:33:00_14:31:00_2017-06-19_16:20:00False2131122222321
8920-999, 3144865.212220.023457631.02017-06-131.6580.039.0Depressive disorder POLYCYSTIC OVARIES, נשאית BRCA12017-06-1813:46:002017-06-1811:18:5520003.02017-06-1307:46:0011:37:00101.0, 171.0, 314421264.0, 31448.0, 315.0, 322138181.02064.01.0General200022017-06-1814:38:5414:51:0015:22:002017-06-1815:35:0046.02017-06-1815:35:002017-06-1817:03:002017-06-1910:10:002017-06-1813:00:002017-06-1815:21:0001.65_80.0_39.0_20002_2017-06-18_14:38:54_15:35:00_14:51:00_15:22:00_15:35:00_17:03:00_2017-06-19_10:10:00False3312333333132
252620-999, 2305465.212220.023462326.02017-09-121.6556.049.0Hypothyroidism Osteoporosis סרטן שד עברה כימו 2017, Lap B.S.O.2017-09-1416:10:002017-09-1413:25:3820001.02017-09-1210:38:0013:53:003739.01.0General200022017-09-1416:22:0016:40:0017:10:002017-09-1417:30:0063.02017-09-1417:34:002017-09-1418:29:002017-09-1513:24:002017-09-1416:10:002017-09-1418:55:0001.65_56.0_49.0_20002_2017-09-14_16:22:00_17:30:00_16:40:00_17:10:00_17:34:00_18:29:00_2017-09-15_13:24:00False3323333333343
373820298226.33190.023452678.02017-03-061.6895.059.0sialolithiasis, תת פעילות של בלוטת התריס2017-03-1008:33:002017-03-1007:16:2020013.02017-03-0620:10:0008:23:0016829.0, 171.0, 265.0, 2982.0, 317388460.0, 322011677.02047.01.0General200142017-03-1009:15:0009:39:0010:29:002017-03-1010:41:0093.02017-03-1010:46:002017-03-1012:32:002017-03-1111:22:002017-03-1007:00:002017-03-1010:06:0001.68_95.0_59.0_20014_2017-03-10_09:15:00_10:41:00_09:39:00_10:29:00_10:46:00_12:32:00_2017-03-11_11:22:00False1141111112211
383920298226.331917.023448972.02017-01-021.8462.044.0SYALOLITHIASIS2017-01-1307:00:002017-01-1305:56:4820009.02017-01-0217:59:0006:15:002982.0, 29894.0, 300861119.0, 304366115.0, 39046503.0, 57691792.01723.01.0General200092017-01-1307:16:0007:36:0009:00:002017-01-1309:18:0093.02017-01-1309:29:002017-01-1310:26:002017-01-1411:16:002017-01-1307:00:002017-01-1308:33:0001.84_62.0_44.0_20009_2017-01-13_07:16:00_09:18:00_07:36:00_09:00:00_09:29:00_10:26:00_2017-01-14_11:16:00False1141111111211
394020298226.331917.023467435.02017-12-251.93129.060.0Diabetes mellitus Hypertension כבד שומני שחמת הכבד-נמצא במעקב מרפאת כבד תל השומר טרומבוציטופניה-PLT 58 ידוע נמצא במעקב בתל השומר נורופתיה ברגליים, LT PAROTIS SYALOLITHASIS2017-12-2910:06:002017-12-2909:26:2020006.02017-12-2516:42:0009:42:00239.0, 266.0, 2982.0, 304047665.0, 36424.02440.017.0MAC200062017-12-2911:22:0011:31:0012:06:002017-12-2912:09:0093.02017-12-2912:13:002017-12-2912:51:002017-12-3013:16:002017-12-2907:00:002017-12-2913:12:0001.93_129.0_60.0_20006_2017-12-29_11:22:00_12:09:00_11:31:00_12:06:00_12:13:00_12:51:00_2017-12-30_13:16:00False2242222222211
404120298226.331918.023454402.02017-04-091.7369.044.0Hypertension, SIALOENDOSCOPY2017-04-1207:00:002017-04-1206:20:2320007.02017-04-0907:40:0006:32:00126.0, 25194887.0, 2982.0, 308237197.0, 317388460.0, 39046503.02305.01.0General200072017-04-1207:04:0007:24:0009:15:002017-04-1209:32:0093.02017-04-1209:35:002017-04-1210:23:002017-04-1311:50:002017-04-1207:00:002017-04-1208:33:0001.73_69.0_44.0_20007_2017-04-12_07:04:00_09:32:00_07:24:00_09:15:00_09:35:00_10:23:00_2017-04-13_11:50:00False1111111111211
414220298226.331920.023452673.02017-03-061.7267.031.0אבנים בבלוטת הרוק שמאל, הפטיטיס אוטואימוני2017-03-1007:00:002017-03-1005:51:1520013.02017-03-1006:10:0006:10:0016829.0, 171.0, 265.0, 2982.0, 317388460.0, 322011677.02047.01.0General200132017-03-1007:20:0007:41:0008:33:002017-03-1008:42:0093.02017-03-1008:49:002017-03-1009:48:002017-03-1111:21:002017-03-1007:00:002017-03-1010:06:0001.72_67.0_31.0_20013_2017-03-10_07:20:00_08:42:00_07:41:00_08:33:00_08:49:00_09:48:00_2017-03-11_11:21:00False1111111111211
patient_idSite CodeMain Surgeon CodeActivity CodeActivity Type CodePlanned SU Time (Large/Medium/Small)Pre-Surgery Patient File (External)Pre-Surgery Admission DateHeightWeightPatient Age (on Surgery Day)Background Diseases/DiagnosesPlanned Surgery DatePlanned Surgery TimeSurgery Admission DateAdministrative Admission TimePlanned Operating Room NumberPre-Surgery Hospitalization Admission DatePre-Surgery Hospitalization Admission TimePre-Surgical Admission Time Before SurgerySurgical Team CodesAnesthesiologist CodeAnesthesia CodeType of AnesthesiaCancellation Reason on Surgery DayActual Operating Room NumberActual Surgery Room Entry DateActual Surgery Room Entry TimeIncision TimeClosure TimeEnd of Surgery Date (Exit from OR)End of Surgery Time (Exit from OR)Planned Surgery DurationRecovery Room Entry DateRecovery Room Entry TimeRecovery Room Exit DateRecovery Room Exit TimePost-Surgery Discharge DatePost-Surgery Discharge TimePlanned Start Date for Doctor BlockPlanned Start Time for Doctor BlockPlanned End Date for Doctor BlockPlanned End Time for Doctor BlockReadmissionstrict_signatureNo ShowPlanned Surgery Time_MinutesAdministrative Admission Time_MinutesPre-Surgery Hospitalization Admission Time_MinutesPre-Surgical Admission Time Before Surgery_MinutesActual Surgery Room Entry Time_MinutesIncision Time_MinutesClosure Time_MinutesEnd of Surgery Time (Exit from OR)_MinutesRecovery Room Entry Time_MinutesRecovery Room Exit Time_MinutesPost-Surgery Discharge Time_MinutesPlanned Start Time for Doctor Block_MinutesPlanned End Time for Doctor Block_Minutes
4400244003202847142.11220.023467511.02017-12-261.67109.040.02017-12-3119:30:002017-12-3116:49:2320005.02017-12-2616:32:0017:08:001407.01.0General200112017-12-3120:42:0020:53:0022:19:002017-12-3122:20:00120.02017-12-3122:24:002017-12-3123:46:002017-12-2616:32:002017-12-3117:05:002017-12-3123:00:0001.67_109.0_40.0_20011_2017-12-31_20:42:00_22:20:00_20:53:00_22:19:00_22:24:00_23:46:00_2017-12-26_16:32:00False4444444444344
4400544006202993326.321920.023462624.02017-09-191.658.046.0parotidectomy2017-09-2517:00:002017-09-2515:11:3220015.02017-09-1907:28:0015:43:00139.0, 276.0, 285.0, 29933.0, 307618652.0, 324.01884.01.0General200152017-09-2517:33:0017:57:0018:24:002017-09-2518:34:00113.02017-09-2518:38:002017-09-2519:06:002017-09-2608:52:002017-09-2517:00:002017-09-2518:53:0001.6_58.0_46.0_20015_2017-09-25_17:33:00_18:34:00_17:57:00_18:24:00_18:38:00_19:06:00_2017-09-26_08:52:00False3413443333143
4400644007203081781.541520.023467236.02017-12-211.785.065.02017-12-3107:00:002017-12-3119:17:4620001.02017-12-2107:24:0006:40:003249.04.0Spinal200012017-12-3107:06:0007:32:0008:27:002017-12-3108:34:00115.02017-12-3108:36:002017-12-3110:54:002017-12-3019:30:002017-12-3107:00:002017-12-3110:50:0001.7_85.0_65.0_20001_2017-12-31_07:06:00_08:34:00_07:32:00_08:27:00_08:36:00_10:54:00_2017-12-30_19:30:00False1411111111411
44015440162032067972385.7817.023465244.02017-11-151.6871.047.0CA OF BREAST2017-11-2711:00:002017-11-2708:40:4420005.02017-11-1510:12:0009:45:00320679723.01667.01.0General200042017-11-2712:02:0012:32:0015:36:002017-11-2715:45:00235.02017-11-2715:47:002017-11-2716:45:002017-11-2911:44:002017-11-2711:00:002017-11-2714:55:0001.68_71.0_47.0_20004_2017-11-27_12:02:00_15:45:00_12:32:00_15:36:00_15:47:00_16:45:00_2017-11-29_11:44:00False2222223333222
4402644027201879668.22211.023450049.02017-01-191.6487.056.0סטרס איקוטננס2017-01-2210:45:002017-01-2207:23:2420013.02017-01-1918:51:0007:42:002068.01.0General200132017-01-2210:40:0010:55:0012:13:002017-01-2212:15:0067.02017-01-2212:19:002017-01-2213:36:002017-01-2313:34:002017-01-2209:30:002017-01-2211:52:0001.64_87.0_56.0_20013_2017-01-22_10:40:00_12:15:00_10:55:00_12:13:00_12:19:00_13:36:00_2017-01-23_13:34:00False2141222222321
44027440282018880571720.023462191.02017-09-101.7267.041.0TU OF BLADDER +הצרות של השופכה2017-09-1221:25:002017-09-1215:03:5320010.02017-09-1014:22:0015:34:002157.01.0General200102017-09-1220:07:0020:27:0021:07:002017-09-1221:29:0095.02017-09-1221:32:002017-09-1223:34:002017-09-1714:26:002017-09-1217:00:002017-09-1223:00:0001.72_67.0_41.0_20010_2017-09-12_20:07:00_21:29:00_20:27:00_21:07:00_21:32:00_23:34:00_2017-09-17_14:26:00False4433444444344
4403044031202063642.31220.023467329.02017-12-241.75123.035.02017-12-3107:00:002017-12-3120:01:3820010.02017-12-2409:06:0006:35:002065.01.0General200102017-12-3107:08:0007:35:0009:19:002017-12-3109:21:00108.02017-12-3109:23:002017-12-3111:47:002017-12-3022:03:002017-12-3107:00:002017-12-3112:54:0001.75_123.0_35.0_20010_2017-12-31_07:08:00_09:21:00_07:35:00_09:19:00_09:23:00_11:47:00_2017-12-30_22:03:00False1421111112411
4403644037202323665.212220.023454103.02017-03-301.62100.059.0CA OF OVARY2017-04-0315:10:002017-04-0311:56:3620010.02017-03-3021:30:0012:25:0023236.01681.01.0General200102017-04-0315:36:0015:55:0016:48:002017-04-0316:54:00161.02017-04-0316:57:002017-04-0318:36:002017-04-0416:32:002017-04-0315:10:002017-04-0319:50:0001.62_100.0_59.0_20010_2017-04-03_15:36:00_16:54:00_15:55:00_16:48:00_16:57:00_18:36:00_2017-04-04_16:32:00False3343333333333
4404144042202940342.31216.023467288.02017-12-211.58125.051.02017-12-3112:05:002017-12-3110:08:2720009.02017-12-2116:17:0010:35:003739.01.0General200092017-12-3114:19:0015:15:0016:31:002017-12-3116:40:00143.02017-12-3116:42:002017-12-3118:18:002017-12-2116:17:002017-12-3112:05:002017-12-3117:00:0001.58_125.0_51.0_20009_2017-12-31_14:19:00_16:40:00_15:15:00_16:31:00_16:42:00_18:18:00_2017-12-21_16:17:00False2242333333333
4404444045203186583.871520.023460317.02017-08-011.8694.068.0RETROCALCANEAL BURSITIS2017-08-0718:45:002017-08-0718:06:2520002.02017-08-0118:35:0018:36:001219.01.0General200022017-08-0720:43:0021:15:0022:27:002017-08-0722:26:00162.02017-08-0722:33:002017-08-0800:08:002017-08-0812:47:002017-08-0717:00:002017-08-0721:27:0001.86_94.0_68.0_20002_2017-08-07_20:43:00_22:26:00_21:15:00_22:27:00_22:33:00_00:08:00_2017-08-08_12:47:00False4444444441244